Abstract: The safety and reliability of low-voltage circuit breakers are the key to the stable operation of power systems, so it is of great significance to predict the degradation trend and evaluate the remaining useful life of circuit breakers. In this paper, based on whale optimization algorithm (WOA) and bidirectional long short-term memory (Bi-LSTM) neural network, a method for predicting the remaining useful life of the circuit breaker operating mechanism is proposed. Firstly, the Pearson correlation coefficient method is used to screen the original monitoring data and the data with higher correlation with the number of circuit breaker openings were selected as the key degradation feature. The health index which can comprehensively characterize the running state of circuit breaker can be obtained by data fusion based on principal component analysis (PCA). The time series of health index is reconstructed by the method of sliding time window, and then the best model obtained by WOA-BiLSTM optimization is used to predict the time series of health index, so as to obtain the multi-step degradation trend of circuit breaker in the future. Finally, according to the set failure threshold, the remaining useful life of the operating mechanism of circuit breaker is determined. Example validation shows that the hybrid prediction model proposed in this paper has a prediction accuracy of up to 96.43%, which is significantly improved compared to other traditional prediction models. So it has certain guiding significance for the actual operation and maintenance of circuit breakers.